The article by Jason W. Osborne from North Carolina State University discusses the importance of data transformations, particularly the Box-Cox transformation, in improving the normality of variables and equalizing variance. Traditional transformations such as square root, log, and inverse are commonly used but can be limited in their effectiveness. The Box-Cox transformation, introduced by Box and Cox in 1964, is a family of power transformations that can optimize the normalization of variables, making it a more robust and flexible approach. The paper highlights that while transformations can improve the robustness of parametric tests and enhance the power of nonparametric tests, they should be used thoughtfully as they alter the nature of the data. The Box-Cox transformation is recommended as a best practice for data normalization due to its ability to simultaneously correct normality, linearity, and homoscedasticity. The article provides examples of applying the Box-Cox transformation to various datasets, including non-normal count data, skewed data from university sizes, and negatively skewed student test grades. Key points include: - The importance of data transformations in meeting statistical assumptions and improving effect sizes. - The limitations of traditional transformations and the benefits of the Box-Cox transformation. - Examples of how the Box-Cox transformation can be applied in SPSS and SAS to find the optimal transformation for each variable. - The need for careful interpretation of transformed data due to the changes in the nature of the variables. The article concludes by emphasizing the practical benefits of the Box-Cox transformation in data cleaning and the availability of modern statistical software to automate the process.The article by Jason W. Osborne from North Carolina State University discusses the importance of data transformations, particularly the Box-Cox transformation, in improving the normality of variables and equalizing variance. Traditional transformations such as square root, log, and inverse are commonly used but can be limited in their effectiveness. The Box-Cox transformation, introduced by Box and Cox in 1964, is a family of power transformations that can optimize the normalization of variables, making it a more robust and flexible approach. The paper highlights that while transformations can improve the robustness of parametric tests and enhance the power of nonparametric tests, they should be used thoughtfully as they alter the nature of the data. The Box-Cox transformation is recommended as a best practice for data normalization due to its ability to simultaneously correct normality, linearity, and homoscedasticity. The article provides examples of applying the Box-Cox transformation to various datasets, including non-normal count data, skewed data from university sizes, and negatively skewed student test grades. Key points include: - The importance of data transformations in meeting statistical assumptions and improving effect sizes. - The limitations of traditional transformations and the benefits of the Box-Cox transformation. - Examples of how the Box-Cox transformation can be applied in SPSS and SAS to find the optimal transformation for each variable. - The need for careful interpretation of transformed data due to the changes in the nature of the variables. The article concludes by emphasizing the practical benefits of the Box-Cox transformation in data cleaning and the availability of modern statistical software to automate the process.